Fix edge case

awq/modules/fused/attn.py

@@ -5,6 +5,7 @@ import logging
 import torch.nn as nn
 import awq_inference_engine
 from torch.nn import functional as F
+from awq.utils.utils import compute_memory_used_pct
 
 try:
     import ft_inference_engine
@@ -164,16 +165,22 @@ class QuantAttentionFused(nn.Module):
                 f"Use: AutoAWQForCausalLM.from_quantized(batch_size={bsz})"
             )
 
-        if self.start_pos > self.max_seq_len:
+        if self.start_pos > self.max_seq_len or self.start_pos + seqlen > self.max_seq_len:
             logging.warning('You have exceeded max_new_tokens, resetting cache...')
             self._initialize_cache(hidden_states.device)
             self.start_pos = 0
 
         elif seqlen > self.max_seq_len:
-            logging.warning('Sequence length > max_seq_len, increasing and resetting cache...')
-            self.max_seq_len *= 2
+            memory_used = compute_memory_used_pct(hidden_states.device)
+
+            if memory_used <= 80:
+                logging.warning('Input sequence length > max_seq_len, increasing and resetting cache...')
+                self.max_seq_len += self.max_seq_len
+                self.attention_shapes = self._get_attention_shapes(None, self.max_seq_len)
                 self._initialize_cache(hidden_states.device)
                 self.start_pos = 0
+            else:
+                logging.error('Input sequence length > max_seq_len, memory is filled, exiting...')
             
         xqkv = self.qkv_proj(hidden_states)
         xqkv = xqkv.view((bsz, seqlen) + self.attention_shapes["xqkv_view"])
@@ -200,8 +207,15 @@ class QuantAttentionFused(nn.Module):
                 .contiguous()
             )
             
+            try:
                 self.cache_v[:bsz, :, self.start_pos : self.start_pos + seqlen, :] = values_store
                 self.cache_k[:bsz, :, :, self.start_pos : self.start_pos + seqlen, :] = keys_store
+            except Exception as ex:
+                print(seqlen, self.max_seq_len)
+                print(self.cache_v.shape, self.cache_v[:bsz, :, self.start_pos : self.start_pos + seqlen, :].shape, values_store.shape)
+                print(self.cache_k.shape, self.cache_k[:bsz, :, :, self.start_pos : self.start_pos + seqlen, :].shape, keys_store.shape)
+                print(ex)
+                exit(0)
 
             if seqlen == 1:
                 xv = self.cache_v[:bsz, :, : self.start_pos + seqlen, :].transpose(1, 2).contiguous()